Data extracted from olive oil mill waste exposed to ambient conditions

Recent studies show that the process of extraction of olive oil results in a large amount of waste. Around 20% the oil is obtained in the process and the remaining 80% corresponds to mainly two types of waste, known as orujo and alperujo. These residues were stored in pools for 6 months in an uncontrolled environment. The reservoirs are open and generate Odorous Volatile Organic Compounds (VOCs) as products of waste decomposition. The data in this article corresponds of physical-chemical compounds of olive oil mill waste exposed to ambient conditions. The data was obtained from two different oil mills, namely, Almazara del Pacífico located in the Alto Pangue area, Talca, Chile; and Agricola y Forestal Don Rafael oil mill, Molina, Chile. Samples were extracted directly from the oil mills to fill 200 L plastic containers that simulated the waste storage in oil mill reservoirs. Each sample was identified and standardized to a mass of 150 kg and moved and stored under uncontrolled ambient conditions at the Universidad de Talca, Curicó, Chile

Prevalencia de leptospirosis en perros de trabajo vacunados y en población humana con riesgo ocupacional

Introduction: Working dogs have been identified as a risk group for developing leptospirosis because they can be infected by Leptospira spp., which can be kept in the renal tubules and interstices for a long time, making them carriers and sources of infection for other hosts, including humans.Objective: To establish the prevalence of Leptospira spp. in vaccinated working dogs and in the occupationally exposed population in six police units in Colombia.Materials and methods: A total of 92 vaccinated dogs (65 males and 27 females) and 69 people from six police units in the municipalities of Manizales, Pereira, Armenia, Ibagué, Tuluá, and Cali were tested. Three structured instruments were applied and blood samples were obtained from people and dogs, which were processed with the microagglutination test (MAT) in 24 serogroups. A clinical examination of the dogs was performed and urine samples were obtained for urine cultures.Results: The seroprevalence of human leptospirosis was 2.9% (n=2) and in dogs, it was 57.61% (n=53). Among the dogs, serogroups L. canicola and L. panama were the most prevalent. Urine cultures were positive in 58.7% (54/92) of the samples. A statistically significant association was found between the age of the dogs (≥10 years; p=0.043) and the location of the police unit (p=0.016) with the urine culture.Conclusion: The epidemiological characteristics of leptospirosis in dogs suggest an endemic presentation of the infection. There is an urgent need to improve current diagnostic methods to investigate canine leptospirosis and differentiate between vaccine and natural infection antibodies.Introducción. Los perros de trabajo pueden infectarse con diversas serovariedades de Leptospira que se mantienen en sus túbulos renales e intersticios por mucho tiempo, por lo que se convierten en portadores y fuentes de infección para otros huéspedes.Objetivo. Establecer la prevalencia de Leptospira spp. en perros de trabajo vacunados y en población humana con riesgo ocupacional de seis unidades policiales en Colombia.Materiales y métodos. Mediante tres instrumentos estructurados, se evaluaron 92 perros de trabajo con inmunización previa contra Leptospira spp. (65 machos y 27 hembras) y 69 personas de seis unidades policiales de los municipios de Manizales, Pereira, Armenia, Ibagué, Tuluá y Cali. Se obtuvieron muestras sanguíneas de las personas y de los perros, las cuales se evaluaron mediante la prueba de microaglutinación (Microscopic Agglutination Test, MAT) en 24 serogrupos. Se hizo un examen clínico de los perros y se obtuvieron muestras de orina para urocultivo.Resultados. La seroprevalencia de leptospirosis en las personas fue de 2,9 % (n=2) y en los perros de 57,61 % (n=53). Los serogrupos más prevalentes en los perros fueron Leptospira canicola y L. panama. El urocultivo fue positivo en 58,7 % (54/92) de las muestras y se encontró asociación estadísticamente significativa entre la edad de los perros (≥10 años; p=0,043) y la ubicación de la unidad policial (p=0,016).Conclusión. Las características epidemiológicas de la leptospirosis en los perros sugieren una presentación endémica de la infección. Se requieren algoritmos diagnósticos sensibles y específicos para investigar la leptospirosis canina y diferenciar los anticuerpos vacunales de la infección natural

Semi-supervised classification using tree-based self-organizing maps

This paper presents a classifier which uses a tree-based Neural Network (NN), and uses both, unlabeled and labeled instances. First, we learn the structure of the data distribution in an unsupervised manner. After convergence, and once labeled data become available, our strategy tags each of the clusters according to the evidence provided by the instances. Unlike other neighborhood-based schemes, our classifier uses only a small set of representatives whose cardinality can be much smaller than that of the input set. Our experiments show that, on average, the accuracy of such classifier is reasonably comparable to those obtained by some of the state-of-the-art classification schemes that only use labeled instances during the training phase. The experiments also show that improved levels of accuracy can be obtained by imposing trees with a larger number of nodes

On using adaptive Binary Search Trees to enhance self organizing maps

We present a strategy by which a Self-OrganizingMap (SOM) with an underlying Binary Search Tree (BST) structure can be adaptively re-structured using conditional rotations. These rotations on the nodes of the tree are local and are performed in constant time, guaranteeing a decrease in the Weighted Path Length (WPL) of the entire tree. As a result, the algorithm, referred to as the Tree-based Topology-Oriented SOM with Conditional Rotations (TTO-CONROT), converges in such a manner that the neurons are ultimately placed in the input space so as to represent its stochastic distribution, and additionally, the neighborhood properties of the neurons suit the best BST that represents the data

A Cluster Analysis of Stock Market Data Using Hierarchical SOMs

The analysis of stock markets has become relevant mainly because of its financial implications. In this paper, we propose a novel methodology for performing a structured cluster analysis of stock market data. Our proposed method uses a tree-based neural network called the TTOSOM. The TTOSOM performs self-organization to construct tree-based clusters of vector data in the multi-dimensional space. The resultant tree possesses interesting mathematical properties such as a succinct representation of the original data distribution, and a preservation of the underlying topology. In order to demonstrate the capabilities of our method, we analyze 206 assets of the Italian stock market. We were able to establish topological relationships between various companies traded on the Italian stock market and visually inspect the resultant taxonomy. The results that we obtained, briefly reported here (but more elaborately in [10]), were amazingly accurate and reflected the real-life relationships between the stocks

Concept Drift Detection Using Online Histogram-Based Bayesian Classifiers

In this paper, we present a novel algorithm that performs online histogram-based classification, i.e., specifically designed for the case when the data is dynamic and its distribution is non-stationary. Our method, called the Online Histogram-based Naïve Bayes Classifier (OHNBC) involves a statistical classifier based on the well-established Bayesian theory, but which makes some assumptions with respect to the independence of the attributes. Moreover, this classifier generates a prediction model using uni-dimensional histograms, whose segments or buckets are fixed in terms of their cardinalities but dynamic in terms of their widths. Additionally, our algorithm invokes the principles of information theory to automatically identify changes in the performance of the classifier, and consequently, forces the reconstruction of the classification model in run-time as and when it is needed. These properties have been confirmed experimentally over numerous data sets (In the interest of space and brevity, we present here only a subset of the available results. More detailed results are found in [2].) from different domains. As far as we know, our histogram-based Naïve Bayes classification paradigm for time-varying datasets is both novel and of a pioneering sort

Concept drift detection using online histogram-based bayesian classifiers

In this paper, we present a novel algorithm that performs online histogram-based classification, i.e., specifically designed for the case when the data is dynamic and its distribution is non-stationary. Our method, called the Online Histogram-based Naïve Bayes Classifier (OHNBC) involves a statistical classifier based on the well-established Bayesian theory, but which makes some assumptions with respect to the independence of the attributes. Moreover, this classifier generates a prediction model using uni-dimensional histograms, whose segments or buckets are fixed in terms of their cardinalities but dynamic in terms of their widths. Additionally, our algorithm invokes the principles of information theory to automatically identify changes in the performance of the classifier, and consequently, forces the reconstruction of the classification model in run-time as and when it is needed. These properties have been confirmed experimentally over numerous data sets (In the interest of space and brevity, we present here only a subset of the available results. More detailed results are found in [2].) from different domains. As far as we know, our histogram-based Naïve Bayes classification paradigm for time-varying datasets is both novel and of a pioneering sort

Inventarios de fauna y flora en relictos de bosque en el enclave seco del río Amaime, Valle del Cauca, Colombia

The dry enclave of Amaime River is one of the few places that remain as Tropical Dry Forest in the Valle del Cauca department, which is considered one of the most threatened ecosystems in the Neotropics. The site has been heavily transformed and threatened by anthropic means like continued burning for the suitability of land for agriculture and livestock, small short rotation crops among others. The aim of the study was to develop detailed inventories of avifauna, herpetofauna, mammalian fauna, and flora in relict sub-xerophytic forests of the enclave, therefore revealing the biodiversity of these remnants, their conservation status and potential threats in order to propose recommendations for conservation. Four sampling areas were selected with cover vegetation as thorny scrub vegetation, riparian forests and forest fragments. For registration and data collection mist nets, transects, direct observations and Sherman traps were used, additionally informal interviews with the community were performed. Despite the reduction of vegetation cover in the dry enclave of Amaime River, the forest fragments serve as shelter and feeding site for both transient species and typical dry and very dry forest wildlife

Inventarios de fauna y flora en relictos de bosque en el enclave seco del río Amaime, Valle del Cauca, Colombia

The dry enclave of Amaime River is one of the few places that remain as Tropical Dry Forest in the Valle del Cauca department, which is considered one of the most threatened ecosystems in the Neotropics. The site has been heavily transformed and threatened by anthropic means like continued burning for the suitability of land for agriculture and livestock, small short rotation crops among others. The aim of the study was to develop detailed inventories of avifauna, herpetofauna, mammalian fauna, and flora in relict sub-xerophytic forests of the enclave, therefore revealing the biodiversity of these remnants, their conservation status and potential threats in order to propose recommendations for conservation. Four sampling areas were selected with cover vegetation as thorny scrub vegetation, riparian forests and forest fragments. For registration and data collection mist nets, transects, direct observations and Sherman traps were used, additionally informal interviews with the community were performed. Despite the reduction of vegetation cover in the dry enclave of Amaime River, the forest fragments serve as shelter and feeding site for both transient species and typical dry and very dry forest wildlife.El enclave seco del río Amaime es uno de los pocos lugares con remanentes de bosques seco tropical en el departamento del Valle del Cauca (Colombia), considerado uno de los ecosistemas más amenazados en el Neotrópico. El enclave se halla fuertemente transformado y amenazado por continuas quemas para la adecuación de tierras para actividades agrícolas y ganaderas, pequeí±os cultivos de rotación rápida entre otras amenazas. El objetivo del estudio fue elaborar inventarios detallados de avifauna, herpetofauna, mastofauna y flora en relictos de bosques subxerofítico en el enclave, que permitan conocer la biodiversidad de estos relictos, su estado de conservación y posibles amenazas con el fin de plantear recomendaciones para su conservación. Se seleccionaron cuatro zonas de muestreo con cobertura vegetal como matorrales espinosos, bosques ribereí±os y fragmentos de bosque. Para el registro y toma de datos se usaron redes de niebla, transectos, observaciones directas, trampas tipo "Sherman" y entrevistas informales con la comunidad. A pesar de la reducción de la cobertura vegetal en el enclave seco del río Amaime, los fragmentos boscosos y matorrales no solo albergan fauna y flora propia de bosques secos y muy secos, sino que también sirven como refugio y sitios de alimentación para especies transitorias